Functional principal component analysis (FPCA) is a technique to
analyze and represent functional data in terms of their main modes of variation
[1]}.
To purely represent amplitude variation, FPCA is most commonly applied
to curves without phase variation, potentially after an initial registration step.
As noted above, the concept of FPCA was also extended to separately
[2]} or
simultaneously [3]} analyze amplitude and phase variation.
Multiple approaches exist for FPCA on sparse or partially observed functional data,
but mostly assume Gaussianity
[4]}.
Adaptations to the non-Gaussian case
for performing generalized FPCA (GFPCA) do exist, but
have to be assessed with care
since marginal estimation of the overall mean can introduce bias
[5]}.
